arXiv Machine Learning June 4, 2026 By Haorui Ma, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel

Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects

Read the original on arXiv Machine Learning →

arXiv:2604. 00915v2 Announce Type: replace Abstract: Estimation of heterogeneous long-term treatment effects (HLTEs) is relevant for personalized decision-making in marketing, economics, and medicine, where short-term observational datasets are often combined with long-term observational datasets.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 19

Benign overfitting beyond prediction: The ordinary least squares interpolator

arXiv:2309. 15769v3 Announce Type: replace-cross Abstract: Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations.

By Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon

safety

arXiv Machine Learning

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Transfer Learning of CATE with Kernel Ridge Regression

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fine-tuning

arXiv Machine Learning

Aug 4

Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts

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By Ying Jin, Ying Jin, Dominik Rothenh\"ausler

safety

arXiv Machine Learning

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Generalization error of min-norm interpolators in transfer learning

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By Yanke Song, Kenneth Gu, Sohom Bhattacharya, Pragya Sur

fine-tuning safety

arXiv Machine Learning

Jul 28

Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions

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By Quyen Tran, Hai Nguyen, Quan Dao, Zhuowei Li, Nam Le, Trung Le, Dimitris Metaxas

benchmarks

arXiv Machine Learning

Jul 7

Reliability and Identifiability in Persona-Trained Monte Carlo: Variance Decomposition, Stability Bounds, and the Identifiability of Heterogeneous News Reaction

arXiv:2607. 04627v1 Announce Type: new Abstract: Persona-Trained Monte Carlo (PTMC) estimates distributions of market-outcome functionals by repeatedly simulating limit-order-book interaction among $K$ neural policy bots whose behavioral personas are drawn from a learned heterogeneity distribution $\mathcal{P}$.

By Salavat Ishbulatov

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